HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding [IEEE TPAMI 2023]
HDGT is an unified heterogeneous transformer-based graph neural network for driving scene encoding. It is a SOTA method on INTERACTION and Waymo Motion Prediction Chanllege.
By time of release in April 2022, the proposed method achieves new state-of-the-art on INTERACTION Prediction Challenge and Waymo Open Motion Challenge, in which we rank the first and second respectively in terms of the minADE/minFDE metric.
All assets and code are under the Apache 2.0 license unless specified otherwise.
If this work is helpful for your research, please consider citing the following BibTeX entry.
@article{jia2023hdgt,
title={HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding},
author={Jia, Xiaosong and Wu, Penghao and Chen, Li and Liu, Yu and Li, Hongyang and Yan, Junchi},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2023},
}
@inproceedings{jia2022temporal,
title={Towards Capturing the Temporal Dynamics for Trajectory Prediction: a Coarse-to-Fine Approach},
author={Jia, Xiaosong and Chen, Li and Wu, Penghao and Zeng, Jia and Yan, Junchi and Li, Hongyang and Qiao, Yu},
booktitle={CoRL},
year={2022}
}